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Related Experiment Video

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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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A multi-scale network for thyroid segmentation and dose assessment in hyperthyroid patients with uncertainty

Chao Wang1, Guang Hu1, Fu Ji1

  • 1Department of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Medical Physics
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning model for accurate thyroid absorbed dose calculation in hyperthyroidism treatment using the Medical Internal Radiation Dose (MIRD) schema. The model enhances segmentation accuracy and quantifies uncertainty, improving personalized dosimetry.

Keywords:
MSRA‐UNet++Monte Carloabsorbed dosehyperthyroidismimage segmentationvoxel phantoms

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Area of Science:

  • Medical Physics
  • Radiological Sciences
  • Artificial Intelligence in Medicine

Background:

  • Accurate absorbed dose assessment is critical for personalized hyperthyroidism treatment using radioactive iodine (131I).
  • Challenges exist in calculating thyroid dose with the Medical Internal Radiation Dose (MIRD) schema, particularly in image segmentation and uncertainty quantification.
  • Comparing dose estimates from MIRD and the Marinelli-Quimby formula requires further investigation.

Purpose of the Study:

  • To design a multi-scale deep learning model for enhanced accuracy and reliability in absorbed dose calculations for hyperthyroid patients based on the MIRD schema.
  • To integrate uncertainty quantification into the segmentation process for improved dosimetry.
  • To compare absorbed dose estimates between the MIRD schema and the Marinelli-Quimby formula.

Main Methods:

  • Developed a UNet++ architecture incorporating multi-scale residual and multi-head attention modules (MSRA-UNet++).
  • Implemented an information entropy-based uncertainty quantification module for segmentation confidence assessment.
  • Created patient-specific thyroid voxel phantoms, calculated S values using Monte Carlo methods, and evaluated absorbed dose using the MIRD schema.

Main Results:

  • The MSRA-UNet++ model achieved high performance in multi-organ segmentation, with a Dice Similarity Coefficient (DSC) of 88.23% and Hausdorff Distance 95% (HD95) of 9.43 pixels for thyroid segmentation.
  • Jaccard Index (JI) improved by 6.17% compared to UNet++ on a clinical dataset, with uncertainty quantified at thyroid edges.
  • The Marinelli-Quimby formula overestimated the MIRD schema absorbed dose by an average of 7.74%.

Conclusions:

  • The MSRA-UNet++ network provides accurate thyroid segmentation with estimated uncertainty, enhancing the reliability of S values through combined segmentation uncertainty and Monte Carlo simulations.
  • This study is the first to incorporate uncertainty quantification into CT image segmentation for hyperthyroid patients, utilizing patient-specific phantoms and radioiodine kinetics for MIRD-based dose assessment.
  • Code will be released on GitHub to facilitate further research and application.